AWS Data Lake
Amazon Web Services is a widely popular Amazon cloud solution with outstanding data lake capabilities. In fact, Amazon S3 is cloud object storage, making it a perfect option for data lakes, as we previously discussed.
Data Lake on AWS architecture simplifies things to the end-user as it automatically configures the cloud servers for data lake use. Users get a convenient, intuitive console that lets them easily search through and request, analyze and transform data sets, as well as configure tags.
There are several advanced AWS Suite services available to use with data lakes, including AWS Lambda (for expanding functions), Amazon OpenSearch (for enabling advanced search options), Amazon Cognito (for user authentication), Amazon Athena (for analysis), and AWS Glue (for transformation).
Because everything works on S3, which is well-known for its scalability, you will never run out of resources or features, making AWS cloud lake a future-proof decision.
All in all, choosing AWS is never a mistake, as it’s a well-established name in the cloud industry, used by some of the biggest companies out there. It has excellent performance, an intuitive console, and a plethora of powerful Amazon apps that will soothe the needs of even the most demanding enterprises.
Azure Data Lake
Azure is Microsoft’s cloud infrastructure with all the capabilities required for running a data lake. It features unlimited data lake size, allowing endless scalability and ensuring even the biggest enterprises can run on Azure platforms without any issues. Azure claims you can store petabyte-size files and trillions of objects while maintaining maximal performance and security.
Azure Data Lake is a fully managed solution, which means you will get support around the clock. They also offer guarantees, ensuring your data remains available at all times. Azure has data encryption, both on-server (HSM keys) and while in transit (SSL), which, combined with multifactor authentication and role-based access controls, ensures your data remains secure.
What’s also great about Azure Data Lake is that it is a part of the Microsoft Cortana Intelligence ecosystem. Suppose you are already working with apps such as Power BI, Azure Synapse Analytics, Visual Studio, Apache Spike, Hive, Storm, or use any kind of Azure SQL. In that case, this data lake will be a perfect choice and will integrate into your existing tech stack seamlessly.
Azure Data Lake has a flexible pricing structure as you can choose to pay for on-demand clusters or a pay-per-job model, which is a better option if you are only going to use the solution sparingly. Because of this, and because all data lakes are open formats, so there are no licenses or recurring fees, you can keep your overhead costs to a minimum.
All in all, Azure Data Lake is a flexible solution that will never let you down and is especially suitable for scaling as it can handle large volumes of data with massive file sizes.
Google Cloud Data Lake
Google Cloud allows you to build a cloud-native data lake, speeding up the access of your analytics and engineer teams without having to upgrade any hardware on-premise.
It works with AI Platform Notebooks but with other non-Google services, such as Apache Spark, BigQuery, GPUs, and other accelerators. In fact, you can migrate any Apache Spark and Hadoop data lakes to Google Cloud, no matter the size, enabling you to have a full-managed data lake cloud solution. Migration is super easy, as you can configure and start the process in as little as 90 seconds.
There are dozens of Google data lake partners with full integration. That means you can expect your data lake to easily integrate and be fully accessible with any app in your tech stack — this is Google, after all.
Lastly, the Google Cloud data lake has flexible pricing with automatic scaling. That means you will never pay for resources you don’t use, but simultaneously, limited resources will never bottleneck your performance and stand in the way of your company’s growth.
The bottom line is that Google Cloud is an excellent data lake provider, with a good number of integrations with non-Google tech stacks and scalable resources, built on the reliable Google server infrastructure, making it a solid pick.